Optimization of Wear Behaviour on Mg-TiO2 Nanocomposite Using Taguchi Grey Relational Analysis

Abstract

n this research, the dry sliding wear behaviour of the Mg-TiO2 nanocomposite is analyzed by conducting a wear test using a pin-on-disc wear testing machine under normal atmospheric conditions. The process parameters considered during the test are the weight fraction of TiO2 nanoparticles, normal load, and sliding speed. The sliding distance and wear track diameter are maintained constant at 1500 m and 90 mm respectively during the test. The performance measures are cumulative wear and coefficient of friction. Taguchi-based Grey relational analysis is employed in this study to optimize the performance of the wear behaviour of the nanocomposite. The design of experiments considered in this study is L9 orthogonal array with each process parameter for three levels. Grey relational grade (GRG) is computed for each experiment and it was found that the maximum GRG of 0.825 is obtained for the process parameter combination A3B2C1 which corresponds to 5wt% TiO2, 1 kg normal load and 1.5 m/s sliding speed respectively. The initial GRG estimated is compared with the predicted and experimental values for the optimum process parameters and it was found that there is an improvement in GRG by 2.2% and 0.77% respectively. ANOVA (Analysis of variance) is carried out to estimate the process parameter that influences the wear behaviour of the nanocomposite significantly and later concluded that the process parameter normal load is the most significant factor other than any other factors.

Keywords

• optimization
• process parameters
• wear
• anova
• grey relational grade